SigSPARQL: Signals as a First-Class Citizen When Querying Knowledge Graphs
Purpose: Cyber-Physical Systems (CPSs) integrate computation and physical processes, producing time series data from thousands of sensors. Knowledge graphs can contextualize these data, yet current approaches that are applicably to monitoring CPS rely on observation-based approaches. This limits the ability to express computations on sensor data, especially when no assumptions can be made about sampling synchronicity or sampling rates. Methodology: We propose an approach for integrating knowledge graphs with signals that model run-time sensor data as functions from time to data. To demonstrate this approach, we introduce SigSPARQL, a query language that can combine RDF data and signals. We assess its technical feasibility with a prototype and demonstrate its use in a typical CPS monitoring use case. Findings: Our approach enables queries to combine graph-based knowledge with signals, overcoming some key limits of observation-based methods. The developed prototype successfully demonstrated feasibility and applicability. Value: This work presents a query-based approach for CPS monitoring that integrates knowledge graphs and signals, alleviating problems of observation-based approaches. By leveraging system knowledge, it enables operators to run a single query across different system instances within the same domain. Future work will extend SigSPARQL with additional signal functions and evaluate it in large-scale CPS deployments.